Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (1904.07695v1)

Abstract: Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm.

• The paper provides a comprehensive review of short text topic modeling techniques by categorizing them into DMM-based, global word co-occurrence, and self-aggregation methods.
• The paper demonstrates that GPU-enhanced DMM models outperform others on classification and clustering tasks, validated using metrics like Purity and NMI across six datasets.
• The paper outlines practical applications and future research directions to enhance content characterization and user engagement in digital platforms.

An Expert Overview of Short Text Topic Modeling Techniques

The paper "Short Text Topic Modeling Techniques, Applications, and Performance: A Survey" systematically compiles and assesses various methodologies developed for short text topic modeling, a growing area of interest in the machine learning community. The paper addresses the significant challenge posed by the sparsity of short texts, such as tweets, snippets, and news headlines, which reduces the efficacy of traditional long text topic models like PLSA and LDA. This summary outlines the key techniques categorized in the paper, examines their computational performance, and discusses their applications and future research directions.

Categorization of Short Text Topic Modeling Techniques

The paper categorizes short text topic modeling techniques into three primary groups:

1. Dirichlet Multinomial Mixture (DMM) Based Methods: These approaches, particularly suitable for short text, assume that each text is generated by a single topic rather than a mixture, as in LDA. Models like GSDMM and its variants (LF-DMM, GPU-DMM, GPU-PDMM) integrate word embeddings into DMM to enhance performance by emphasizing semantic relationships.
2. Global Word Co-occurrence Based Methods: Techniques such as Biterm Topic Model (BTM) and Word Network Topic Model (WNTM) utilize co-occurrence patterns in the global text corpus to mitigate short text sparsity issues. These models leverage the semantic connection between words observed in adjacent windows or as captured through network-based approaches.
3. Self-Aggregation Based Methods: These methods, such as SATM and PTM, cluster short texts into pseudo-documents to create richer semantic contexts before applying traditional topic modeling. The necessity of merging texts without auxiliary metadata poses practical challenges, but these models aim to optimize both aggregation and topic modeling simultaneously.

Performance Evaluation

The paper conducts an extensive experimental comparison using six distinct datasets, measuring the efficacy of each model based on classification accuracy, clustering quality (using Purity and NMI), and topic coherence. The results highlight:

• The superiority of GPU-enhanced DMM models in general datasets, attributed to their ability to incorporate semantic embeddings.
• Global co-occurrence models demonstrated effectiveness in datasets with abundant co-occurrence information, proving particularly competitive in tasks like clustering.
• Despite higher computational costs and complexity, sophisticated embedding-based models exhibit notable robustness across various metrics.

Practical Implications and Applications

Short text topic modeling supports numerous applications, such as content characterization, personalized recommendation systems, and social media analysis. The adapted methodologies facilitate accurate topic detection and classification in contexts where short text data prevails, offering potential directions for improving information retrieval and user engagement in digital platforms.

Future Directions in Short Text Topic Modeling

The emerging field of short text topic modeling paves the way for several research opportunities:

• Visualization: Enhancing interpretability through improved topic representation and document visualization methods.
• Evaluation Metrics: Developing comprehensive metrics that reflect the multifaceted use-cases and discriminate the subtleties of short text topics more effectively.
• Model Selection and Customization: Offering guideline-based model selection strategies for specific datasets or application contexts, and enabling dynamic adaptability of models for varying data characteristics.

In conclusion, the paper contributes significantly to consolidating existing knowledge on short text topic modeling, encouraging advanced research and practical deployment of these methodologies. As this field matures, these techniques will increasingly enrich the mining and interpretation of vast quantities of short text data prevalent in today's digital ecosystems.